Abstract for shadafan_tr157

A SYSTOLIC ARRAY IMPLEMENTATION OF A DYNAMIC SEQUENTIAL NEURAL
NETWORK FOR PATTERN RECOGNITION

R. S. Shadafan and M. Niranjan

December 1993

Recently we have developed a sequential algorithm for designing a
multi-layer perceptron classifier~\cite{rn:ICNN93,tr:127}. Our
approach, called Sequential Input Space Partitioning (SISP) algorithm,
results in a one pass algorithm and a growing network. We exploit the
fact that class boundary constructed by an MLP classifier is piecewise
linear and hence the contribution of each hidden hidden unit to the
final decision is essentially local . We have shown that, in a number
of benchmark classification problems, the algorithm achieves
performances similar to conventional batch methods of training. We
have also argued that the sequential design has an indirect
computational advantage. This computational advantage comes from the
fact that the algorithm sees each data item only once, hence the
feasibility of pipelining the training procedures in a true parallel
architecture. In this paper, we show how this one pass algorithm can
be pipelined and realised by a systolic array implementation.

The idea is to exploit the fact that the locations of boundary
segments are determined by solving localised classification problems.
Training is achieved by updating local covariances using the Recursive
Least Squares (RLS) algorithm. The algorithm is sequential in the
sense that training examples are passed only once, and the network
will learn and/or expand at the arrival of each example. The major
advantage in this sequential scheme is the feasibility of pipelining
the training procedures in a true parallel architecture. In this
paper, we present a systolic array implementation of the SISP
algorithm.

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